4 citations · 4 across the 11 of their papers we have counts for
12 papers
Safe Task Planning with Long-Term Graph Memory for Embodied Agents
Siyuan Li, Taiyan Lang, Aoqi Yan +6
Large language models (LLMs) and vision-language models (VLMs) have significantly advanced zero-shot task planning for embodied agents. However, most LLM- and VLM-driven methods st…
ConceptTree: Bringing Semantic Transparency to Black-Box Decision Making for Robotic Manipulation
Yongyan Wen, Feifan Liu, Jinyi Chen +3
Establishing interpretable decision-making processes in long-horizon robotic manipulation is critical for enabling reliable human oversight and intervention. However, existing appr…
Curriculum reinforcement learning with measurable task representation learning
Yongyan Wen, Siyuan Li, Mingjian Fu +3
In curriculum reinforcement learning (CRL), an agent incrementally accumulates knowledge over a sequence of tasks (i.e., a curriculum), and the learning process is aimed at using t…
Hierarchical Audio-Visual-Proprioceptive Fusion for Precise Robotic Manipulation
Siyuan Li, Jiani Lu, Yu Song +3
Existing robotic manipulation methods primarily rely on visual and proprioceptive observations, which may struggle to infer contact-related interaction states in partially observab…
Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming
Siyuan Li, Yifan Yu, Zhihao Zhang +5
Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Conventional MILP solving mainly relies on carefully designed heuristics embed…
DHEvo: Data-Algorithm Based Heuristic Evolution for Generalizable MILP Solving
Zhihao Zhang, Siyuan Li, Chenxi Li +6
Primal heuristics play a critical role in improving the efficiency of mixed integer programming (MILP) solvers. As large language models (LLMs) have demonstrated superior code gene…
Unsupervised Skill Discovery through Skill Regions Differentiation
Ting Xiao, Jiakun Zheng, Rushuai Yang +4
Unsupervised Reinforcement Learning (RL) aims to discover diverse behaviors that can accelerate the learning of downstream tasks. Previous methods typically focus on entropy-based…
SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
Yongyan Wen, Siyuan Li, Rongchang Zuo +3
Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which lim…
Safe Planner: Empowering Safety Awareness in Large Pre-Trained Models for Robot Task Planning
Siyuan Li, Zhe Ma, Feifan Liu +7
Robot task planning is an important problem for autonomous robots in long-horizon challenging tasks. As large pre-trained models have demonstrated superior planning ability, recent…
An Imitative Reinforcement Learning Framework for Pursuit-Lock-Launch Missions
Siyuan Li, Rongchang Zuo, Bofei Liu +3
Unmanned Combat Aerial Vehicle (UCAV) Within-Visual-Range (WVR) engagement, referring to a fight between two or more UCAVs at close quarters, plays a decisive role on the aerial ba…
Auxiliary Reward Generation with Transition Distance Representation Learning
Siyuan Li, Shijie Han, Yingnan Zhao +2
Reinforcement learning (RL) has shown its strength in challenging sequential decision-making problems. The reward function in RL is crucial to the learning performance, as it serve…
Robust Visual Imitation Learning with Inverse Dynamics Representations
Siyuan Li, Xun Wang, Rongchang Zuo +5
Imitation learning (IL) has achieved considerable success in solving complex sequential decision-making problems. However, current IL methods mainly assume that the environment for…